Analysis of dogs’ sleep patterns using convolutional neural networks

Anna Zamansky, Aleksandr M. Sinitca, Dmitry I. Kaplun, Michael Plazner, Ivana G. Schork, Robert J. Young, Cristiano S. de Azevedo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Video-based analysis is one of the most important tools of animal behavior and animal welfare scientists. While automatic analysis systems exist for many species, this problem has not yet been adequately addressed for one of the most studied species in animal science—dogs. In this paper we describe a system developed for analyzing sleeping patterns of kenneled dogs, which may serve as indicator of their welfare. The system combines convolutional neural networks with classical data processing methods, and works with very low quality video from cameras installed in dogs shelters.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publicationImage Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
EditorsIgor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030305079
StatePublished - 2019
Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
Duration: 17 Sep 201919 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11729 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Artificial Neural Networks, ICANN 2019

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Animal science
  • Animal welfare
  • Computer vision
  • Convolutional neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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